




"""simple docstring"""




import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
					import transformers



def        a__					(							snake_case__							)		->   List[str]:
			random.seed(snake_case__							)
			np.random.seed(snake_case__							)
			torch.manual_seed(snake_case__							)
			torch.cuda.manual_seed_all(snake_case__							)
			# ^^ safe to call this function even if cuda is not available






class        __magic_name__				:






				'''simple docstring'''





				def __init__(			self						,					_a						,					_a = 0.9_999						,					_a = 0.0						,					_a = 0						,					_a = False						,					_a = 1.0						,					_a = 2 / 3						,					_a = None						,					_a = None						,					**_a						,					):





							"""simple docstring"""

							if isinstance(_a						,					torch.nn.Module					):
										lowerCamelCase      =   (
										    """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
										    """Please pass the parameters of the module instead."""
										)
										deprecate(
										    """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""						,					"""1.0.0"""						,					_a						,					standard_warn=_a						,					)
										lowerCamelCase      =   parameters.parameters()

										# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
										lowerCamelCase      =   True

							if kwargs.get("""max_value"""						,					_a					) is not None:
										lowerCamelCase      =   """The `max_value` argument is deprecated. Please use `decay` instead."""
										deprecate("""max_value"""						,					"""1.0.0"""						,					_a						,					standard_warn=_a					)
										lowerCamelCase      =   kwargs["""max_value"""]

							if kwargs.get("""min_value"""						,					_a					) is not None:
										lowerCamelCase      =   """The `min_value` argument is deprecated. Please use `min_decay` instead."""
										deprecate("""min_value"""						,					"""1.0.0"""						,					_a						,					standard_warn=_a					)
										lowerCamelCase      =   kwargs["""min_value"""]

							lowerCamelCase      =   list(_a					)
							lowerCamelCase      =   [p.clone().detach() for p in parameters]

							if kwargs.get("""device"""						,					_a					) is not None:
										lowerCamelCase      =   """The `device` argument is deprecated. Please use `to` instead."""
										deprecate("""device"""						,					"""1.0.0"""						,					_a						,					standard_warn=_a					)
										self.to(device=kwargs["""device"""]					)

							lowerCamelCase      =   None

							lowerCamelCase      =   decay
							lowerCamelCase      =   min_decay
							lowerCamelCase      =   update_after_step
							lowerCamelCase      =   use_ema_warmup
							lowerCamelCase      =   inv_gamma
							lowerCamelCase      =   power
							lowerCamelCase      =   0
							lowerCamelCase      =   None  # set in `step()`

							lowerCamelCase      =   model_cls
							lowerCamelCase      =   model_config





				@classmethod
				def   _lowerCAmelCase							(			cls						,					_a						,					_a					):





							"""simple docstring"""
							lowerCamelCase ,  lowerCamelCase      =   model_cls.load_config(_a						,					return_unused_kwargs=_a					)
							lowerCamelCase      =   model_cls.from_pretrained(_a					)

							lowerCamelCase      =   cls(model.parameters()						,					model_cls=_a						,					model_config=model.config					)

							ema_model.load_state_dict(_a					)
							return ema_model





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if self.model_cls is None:
										raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""					)

							if self.model_config is None:
										raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""					)

							lowerCamelCase      =   self.model_cls.from_config(self.model_config					)
							lowerCamelCase      =   self.state_dict()
							state_dict.pop("""shadow_params"""						,					_a					)

							model.register_to_config(**_a					)
							self.copy_to(model.parameters()					)
							model.save_pretrained(_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   max(0						,					optimization_step - self.update_after_step - 1					)

							if step <= 0:
										return 0.0

							if self.use_ema_warmup:
										lowerCamelCase      =   1 - (1 + step / self.inv_gamma) ** -self.power
							else:
										lowerCamelCase      =   (1 + step) / (10 + step)

							lowerCamelCase      =   min(_a						,					self.decay					)
							# make sure decay is not smaller than min_decay
							lowerCamelCase      =   max(_a						,					self.min_decay					)
							return cur_decay_value





				@torch.no_grad()
				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if isinstance(_a						,					torch.nn.Module					):
										lowerCamelCase      =   (
										    """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
										    """Please pass the parameters of the module instead."""
										)
										deprecate(
										    """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""						,					"""1.0.0"""						,					_a						,					standard_warn=_a						,					)
										lowerCamelCase      =   parameters.parameters()

							lowerCamelCase      =   list(_a					)

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							lowerCamelCase      =   self.get_decay(self.optimization_step					)
							lowerCamelCase      =   decay
							lowerCamelCase      =   1 - decay

							lowerCamelCase      =   contextlib.nullcontext
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
										import deepspeed

							for s_param, param in zip(self.shadow_params						,					_a					):
										if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
													lowerCamelCase      =   deepspeed.zero.GatheredParameters(_a						,					modifier_rank=_a					)

										with context_manager():
													if param.requires_grad:
																s_param.sub_(one_minus_decay * (s_param - param)					)
													else:
																s_param.copy_(_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   list(_a					)
							for s_param, param in zip(self.shadow_params						,					_a					):
										param.data.copy_(s_param.to(param.device					).data					)





				def   _lowerCAmelCase							(			self						,					_a=None						,					_a=None					):





							"""simple docstring"""
							lowerCamelCase      =   [
							    p.to(device=_a						,					dtype=_a					) if p.is_floating_point() else p.to(device=_a					)
							    for p in self.shadow_params
							]





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return {
							    "decay": self.decay,
							    "min_decay": self.min_decay,
							    "optimization_step": self.optimization_step,
							    "update_after_step": self.update_after_step,
							    "use_ema_warmup": self.use_ema_warmup,
							    "inv_gamma": self.inv_gamma,
							    "power": self.power,
							    "shadow_params": self.shadow_params,
							}





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   [param.detach().cpu().clone() for param in parameters]





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if self.temp_stored_params is None:
										raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""					)
							for c_param, param in zip(self.temp_stored_params						,					_a					):
										param.data.copy_(c_param.data					)

							# Better memory-wise.
							lowerCamelCase      =   None





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   copy.deepcopy(_a					)

							lowerCamelCase      =   state_dict.get("""decay"""						,					self.decay					)
							if self.decay < 0.0 or self.decay > 1.0:
										raise ValueError("""Decay must be between 0 and 1"""					)

							lowerCamelCase      =   state_dict.get("""min_decay"""						,					self.min_decay					)
							if not isinstance(self.min_decay						,					_a					):
										raise ValueError("""Invalid min_decay"""					)

							lowerCamelCase      =   state_dict.get("""optimization_step"""						,					self.optimization_step					)
							if not isinstance(self.optimization_step						,					_a					):
										raise ValueError("""Invalid optimization_step"""					)

							lowerCamelCase      =   state_dict.get("""update_after_step"""						,					self.update_after_step					)
							if not isinstance(self.update_after_step						,					_a					):
										raise ValueError("""Invalid update_after_step"""					)

							lowerCamelCase      =   state_dict.get("""use_ema_warmup"""						,					self.use_ema_warmup					)
							if not isinstance(self.use_ema_warmup						,					_a					):
										raise ValueError("""Invalid use_ema_warmup"""					)

							lowerCamelCase      =   state_dict.get("""inv_gamma"""						,					self.inv_gamma					)
							if not isinstance(self.inv_gamma						,					(float, int)					):
										raise ValueError("""Invalid inv_gamma"""					)

							lowerCamelCase      =   state_dict.get("""power"""						,					self.power					)
							if not isinstance(self.power						,					(float, int)					):
										raise ValueError("""Invalid power"""					)

							lowerCamelCase      =   state_dict.get("""shadow_params"""						,					_a					)
							if shadow_params is not None:
										lowerCamelCase      =   shadow_params
										if not isinstance(self.shadow_params						,					_a					):
													raise ValueError("""shadow_params must be a list"""					)
										if not all(isinstance(_a						,					torch.Tensor					) for p in self.shadow_params					):
													raise ValueError("""shadow_params must all be Tensors"""					)









"""simple docstring"""




from collections.abc import Sequence



def        a__					(							snake_case__			,					snake_case__ = False							)		->   float:
			if not arr:
						return 0

			lowerCamelCase      =   0 if allow_empty_subarrays else float("""-inf"""							)
			lowerCamelCase      =   0.0
			for num in arr:
						lowerCamelCase      =   max(0 if allow_empty_subarrays else num			,					curr_sum + num							)
						lowerCamelCase      =   max(snake_case__			,					snake_case__							)

			return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					lowerCAmelCase				:     Union[str, Any]					=				[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")



